◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Mohit Bansal

7 papers hereh-index 5150 citations14 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5

Across the 5 of 7 papers where every author was matched, so the position is known.

fields
  • cs.CL4
  • cs.AI1
  • cs.CV1
  • cs.LG1
same name
  • Mohit Bansal — 55 papers, h 20
  • Mohit Bansal — 29 papers, h 15
  • Mohit Bansal — 10 papers, h 5
  • Mohit Bansal — 9 papers, h 7
  • Mohit Bansal — 8 papers, h 2
  • Mohit Bansal — 7 papers, h 12

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2025

CoKe: Customizable Fine-Grained Story Evaluation via Chain-of-Keyword Rationalization

Brihi Joshi, Sriram Venkatapathy, Mohit Bansal +2

Evaluating creative text such as human-written stories using language models has always been a challenging task -- owing to the subjectivity of multi-annotator ratings. To mimic th…

cs.CL2024

Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM

Haw-Shiuan Chang, Nanyun Peng, Mohit Bansal +2

Contrastive decoding (CD) (Li et al., 2023) improves the next-token distribution of a large expert language model (LM) using a small amateur LM. Although CD is applied to various L…

cs.CL2024

LLM Self-Correction with DeCRIM: Decompose, Critique, and Refine for Enhanced Following of Instructions with Multiple Constraints

Thomas Palmeira Ferraz, Kartik Mehta, Yu-Hsiang Lin +7

Instruction following is a key capability for LLMs. However, recent studies have shown that LLMs often struggle with instructions containing multiple constraints (e.g. a request to…

cs.CL2024

REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic Entropy

Haw-Shiuan Chang, Nanyun Peng, Mohit Bansal +2

Decoding methods for large language models (LLMs) usually struggle with the tradeoff between ensuring factuality and maintaining diversity. For example, a higher p threshold in the…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.